Predicting piezometric water level in dams via artificial neural networks

Neural Computing and Applications

Volumn 24, Issue 5, 2014, Pages 1115-1121

  Ranković, Vesna ^a   Novaković, Aleksandar ^a   Grujović, Nenad ^a   Divac, Dejan ^b   Milivojević, Nikola ^b  

^a UNIVERSITY OF KRAGUJEVAC   (Serbia)

^b Jaroslav erni Institute for the Development of Water Resources   (Serbia)

Author keywords

Dam; IRPROP+; Neural network; Piezometric water level

Indexed keywords

GEOLOGICAL FORMATION; HEALTH MONITORING; IRPROP; LOADING CONDITION; MULTIPLE LINEAR REGRESSION MODELS; PIEZOMETRIC LEVELS; RESILIENT PROPAGATION ALGORITHM; TEMPERATURE VARIATION;

FEEDFORWARD NEURAL NETWORKS; LINEAR REGRESSION; MATHEMATICAL MODELS; NEURAL NETWORKS; SEEPAGE; WATER LEVELS;

DAMS;

EID: 84900672832     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-1334-2     Document Type: Article

References (33)

1. Malkawi AIH, Al-Sheriadeh M (2000) Evaluation and rehabilitation of dam seepage problems. A case study: Kafrein dam. Eng Geol 56: 335-345.
2. Turkmen S, Öeguler E, Taga H, Karaogullarindan T (2002) Detection and evaluation of horizontal fractures in earth dams using the self-potential method. Eng Geol 63: 247-257.
3. Turkmen S, Ozguler E, Taga H, Karaogullarindan T (2002) Seepage problems in the Karstic limestone foundation of the Kalecik Dam (south Turkey). Eng Geol 63(3-4): 247-257.
4. Panthulu TV, Krishnaiah C, Shirke JM (2001) Detection of seepage paths in earth dams using self-potential and electrical resistivity methods. Eng Geol 59(3-4): 281-295.
5. Uromeihy A, Barzegari G (2007) Evaluation and treatment of seepage problems at Chapar-Abad Dam. Iran. Eng Geol 91(2-4): 219-228.
6. Ayvaz MT, Karahan H (2007) Modeling three-dimensional free-surface flows using multiple spreadsheets. Comput Geotech 34(2): 112-123.
7. Bardet JP, Tobita T (2002) A practical method for solving free-surface seepage problems. Comput Geotech 29(6): 451-475.
8. Darbandi M, Torabi SO, Saadat M, Daghighi Y, Jarrahbashi D (2007) A moving-mesh finite-volume method to solve free-surface seepage problem in arbitrary geometries. Int J Numer Anal Met 31: 1609-1629.
9. Ouria A, Toufigh MM, Nakhai A (2007) An Investigation on the effect of the coupled and uncoupled formulation on transient seepage by the finite element method. Am J Appl Sci 4(12): 950-956.
10. Chen JT, Hong HK, Chyuan SW (1994) Boundary element analysis and design in seepage problems using dual integral formulation. Finite Elem Anal Des 17(1): l-l20.
11. Peng T-R, Wang C-H (2008) Identification of sources and causes of leakage on a zoned earth dam in northern Taiwan: hydrological and isotopic evidence. Appl Geochem 23(8): 2438-2451.
12. Xijian W, Haibin T, Riyun L (2011) Locating the plane concentrated seepage in dam by transient temperature field. Proc Eng 26: 1749-1755.
13. Mohammadi Z (2009) Assessing hydrochemical evolution of groundwater in limestone terrain via principal component analysis. Environ Earth Sci 59: 429-439.
14. Kabiri-Samani AR, Aghaee-Tarazjani J, Borghei SM, Jeng DS (2011) Application of neural networks and fuzzy logic models to long-shore sediment transport. Appl Soft Comput 11(2): 2880-2887.
15. Tayfur G (2002) Artificial neural networks for sheet sediment transport. Hydrolog Sci J 47(6): 879-892.
16. Yang CT, Marsooli R, Aalami MT (2009) Evaluation of total load sediment transport formulas using ANN. Int J Sediment Res 24(3): 274-286.
17. Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1-4): 28-40.
18. Corsini A, Cervi F, Ronchetti F (2009) Weight of evidence and artificial neural networks for potential groundwater spring mapping: an application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology 111(1-2): 79-87.
19. Hu ZY, Huang GH, Chan CW (2003) A fuzzy process controller for in situ groundwater bioremediation. Eng Appl Artif Intel 16(2): 131-147.
20. Taormina R, K-w Chau, Sethi R (2012) Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng Appl Artif Intel. doi: 10. 1016/j. engappai. 2012. 02. 009.
21. Mata J (2011) Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng Struct 33(3): 903-910.
22. Ranković V, Grujović N, Divac D, Milivojević N, Novaković A (2012) Modelling of dam behaviour based on neuro-fuzzy identification. Eng Struct 35: 107-113.
23. Gholizadeh S, Seyedpoor SM (2011) Shape optimization of arch dams by metaheuristics and neural networks for frequency constraints. Sci Iran 18(5): 1020-1027.
24. Wang BS, He ZC (2007) Crack detection of arch dam using statistical neural network based on the reductions of natural frequencies. J Sound Vib 302(4-5): 1037-1047.
25. Monjezi M, Hasanipanah M, Khandelwal M (2012) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl. doi: 10. 1007/s00521-012-0856-y.
26. Tayfur G, Swiatek D, Wita A, Singh VP (2005) Case study: finite element method and artificial neural network models for flow through Jeziorsko Earthfill Dam in Poland. J Hydraul Eng 131(6): 431-440.
27. Bonelli S, Royet P (2001) Delayed response analysis of dam monitoring data. In: Proceedings of the ICOLD European symposium on Dams in a European Context, GEIRANGER: Norvège, pp 91-99.
28. Opyrchal L (2003) Application of fuzzy sets method to identify seepage path through Dams. J Hydraul Eng-ASCE 129(7): 546-548.
29. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River.
30. Rumelhart DE, Hinton GE, Wiliams RJ (1986) Learning representations by back-propagating errors. Nature 323: 533-536.
31. Riedmiller M (1994) Advanced supervised learning in multi-layer perceptrons-from backpropagation to adaptive learning algorithms. Comput Stand Inter 16(5): 265-278.
32. Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Ruspini EH (ed) Proceedings of the IEEE international conference on neural networks, IEEE Press, New York, pp 586-591.
33. Igel C, Husken M (2002) Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing 50: 105-123.